The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.
Fundamentals of Machine Learning in Finance
This course is part of Machine Learning and Reinforcement Learning in Finance Specialization
Instructor: Igor Halperin
Sponsored by Coursera for Reliance Family
21,521 already enrolled
(336 reviews)
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There are 4 modules in this course
What's included
9 videos4 readings1 programming assignment1 ungraded lab
What's included
6 videos3 readings1 programming assignment1 ungraded lab
What's included
7 videos3 readings1 programming assignment1 ungraded lab
What's included
11 videos3 readings1 programming assignment1 ungraded lab
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Reviewed on Aug 9, 2019
Furthered my understanding of how probabilistic models are connected to Machine Learning models. Very happy with the content in this course.
Reviewed on Dec 24, 2018
So far so good. The lecturer refers to projects of which some weren't covered in this course. So a little confusing. Takes lots of googling to finish this course.
Reviewed on Sep 18, 2019
This is a great course, I strongly recommend. However, the assignments take a while to finish.
Recommended if you're interested in Data Science
Indian School of Business
Coursera Instructor Network
University of London
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